Stockfish does not steal research or scoop researchers.
The concentration of computing resources and capital should be examined by the math community.
Frames could literally cost 1 dollar (only happened after china entered this market), but good luck finding ones like this with good lenses.
You need to buy from one supplier who intentionally offers cheapest frames for 50+ dollars and those frames look like crap. The ones that look better (even if same plastic) cost 500+ dollars - and all due to price gouging.
For lenses I am not sure, but suspect something similar.
Fielmann in Germany has been offering very reasonably priced glasses and frames for ages.
- I think competitive open models are just an artifact of the AI race we're witnessing right now. What's the incentive for a company to spend billions researching, developing, and training a model, only to release it for free? Leading to the next point.
- Even if open models are good enough to be competitive, how are we going to run them? Doing so locally is next to impossible and I don't see that changing. The capabilities of models that you can run locally will always get better, of course, but the level of quality that is considered essential for work will always stay pinned at "near-frontier". Datacenters will always have better optimization and economies of scale, the industry will consolidate over time and eventually we'll end up with a handful of companies that operate the hardware serving 95% of all inference needs.
I'm not convinced that we can reach the fantasy world that they're trying to sell without killing the entire planet, but if we somehow do I don't see how we can avoid the world turning into a dystopian hellscape. We would need extremely radical interventions to avoid that scenario, such that these models and the hardware to run them would be owned and governed democratically, i.e. the end of capitalism.
And a lot of people are using trailing edge models just fine already.
> [...] but the level of quality that is considered essential for work will always stay pinned at "near-frontier".
Why? When we'll finally all write our software in Lean and prove it correct and prove it fast, it won't matter that a slightly more clever model could have found a slightly nicer proof or whatever.
Just like today people happily use Python for many programs, even though rewriting in C might give you a performance boost. Good enough is often good enough.
If that's your only objection: in a few years you can prove Rieman's hypothesis on your smartphone, no need for any trillionaires to give you permission. Does that make any change to your argument, or did it not actually matter?
For the latter: I assume that having a whole data centre will always be an advantage. I am saying that for a fixed target, like proving the Rieman hypothesis from scratch, the required hardware will shrink.
And, yes, the Rieman hypothesis hasn't been proven yet. So to take your fears into account, replace my example with something they've already done, like constructing a solution to the Navier-Stokes-problem.
Yes, but to what point the hardware will shrink? There are several orders of magnitude of difference in what in your mind AI will become and what more conservative people believe. You take your view as granted...